As video services (e.g., video cellular phone, video conference, video on demand, Internet protocol television, and distance-learning) are employed in packet networks (e.g., next generation networks, triple-play networks, or 3rd generation networks), there is a need to measure in-service video quality transmitted over packet networks in real time.
There are two kinds of VQM methods: subjective method and objective method. Subjective VQM methods are based on evaluations made by human subjects under well-defined and controlled conditions. Subjective methods are very costly and time-consuming. In addition, the processes of subjective methods cannot be automated.
An objective VQM method employs a given mathematical, physical, or psycho-psychological model to evaluate video quality. The objective methods include a full-reference (FR) objective VQM method, a reduced-reference (RF) objective VQM method, and a no-reference (NR) objective VQM method. Both the FR and the RF methods need to make a reference to the original video (i.e. the video actually transmitted from the transmitting side) and cannot be used for real-time in-service video quality measurement. The NR methods, on the other hand, do not need to make a reference to the original video. Instead, the NR methods make observations only on decoded video (i.e. the video that has been received and decoded on the receiving side) and estimate the video quality using only the observed information on the decoded video. This makes the NR methods suitable for real-time in-service VQM in realistic network environment.
The NR methods are generally classified into two categories. The first one refers to a NR method using image metric and the second one refers to a NR method using packet metric.
The image metric-based NR method evaluates video quality by analyzing the image features of video images that the end users received, such as jerkiness, blockiness, blurriness, spatial information, and temporal information. One disadvantage of this method is its high computational complexity. This makes it not suitable for real-time applications, especially for mobile device with limited computational resource. The other drawback is that it cannot be applied to network troubleshooting.
The packet metric-based NR method evaluates video quality by measuring packet-level characteristics of video streams, such as jitter, delay, packet loss. One disadvantage of this method is its inability to correlate well with human perception. Although prior proposals have been made to overcome the problem, they are still incapable of capturing video impairments caused by the source, a very typical and common issue in video cellular phone and video conference applications.
Thus, what is needed is a NR method and system that measures video quality in real time without the above-mentioned drawbacks.